code: def transform_config(
      transformer,
      data,
      data_type="S3Prefix",
      content_type=None,
      compression_type=None,
      split_type=None,
      job_name=None,
      input_filter=None,
      output_filter=None,
      join_source=None,
  ):
      """Export Airflow transform config from a SageMaker transformer
  
      Args:
          transformer (sagemaker.transformer.Transformer): The SageMaker
              transformer to export Airflow config from.
          data (str): Input data location in S3.
          data_type (str): What the S3 location defines (default: 'S3Prefix').
              Valid values:
  
              * 'S3Prefix' - the S3 URI defines a key name prefix. All objects with this prefix will
                    be used as inputs for the transform job.
  
              * 'ManifestFile' - the S3 URI points to a single manifest file listing each S3 object
                    to use as an input for the transform job.
  
          content_type (str): MIME type of the input data (default: None).
          compression_type (str): Compression type of the input data, if
              compressed (default: None). Valid values: 'Gzip', None.
          split_type (str): The record delimiter for the input object (default:
              'None'). Valid values: 'None', 'Line', 'RecordIO', and 'TFRecord'.
          job_name (str): job name (default: None). If not specified, one will be
              generated.
          input_filter (str): A JSONPath to select a portion of the input to
              pass to the algorithm container for inference. If you omit the
              field, it gets the value '$', representing the entire input.
              For CSV data, each row is taken as a JSON array,
              so only index-based JSONPaths can be applied, e.g. $[0], $[1:].
              CSV data should follow the `RFC format <https://tools.ietf.org/html/rfc4180>`_.
              See `Supported JSONPath Operators
              <https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html#data-processing-operators>`_
              for a table of supported JSONPath operators.
              For more information, see the SageMaker API documentation for
              `CreateTransformJob
              <https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateTransformJob.html>`_.
              Some examples: "$[1:]", "$.features" (default: None).
          output_filter (str): A JSONPath to select a portion of the
              joined/original output to return as the output.
              For more information, see the SageMaker API documentation for
              `CreateTransformJob
              <https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateTransformJob.html>`_.
              Some examples: "$[1:]", "$.prediction" (default: None).
          join_source (str): The source of data to be joined to the transform
              output. It can be set to 'Input' meaning the entire input record
              will be joined to the inference result. You can use OutputFilter
              to select the useful portion before uploading to S3. (default:
              None). Valid values: Input, None.
  
      Returns:
          dict: Transform config that can be directly used by
          SageMakerTransformOperator in Airflow.
      """
      if job_name is not None:
          transformer._current_job_name = job_name
      else:
          base_name = transformer.base_transform_job_name
          transformer._current_job_name = (
              utils.name_from_base(base_name)
              if base_name is not None
              else transformer.model_name
          )
  
      if transformer.output_path is None:
          transformer.output_path = s3.s3_path_join(
              "s3://",
              transformer.sagemaker_session.default_bucket(),
              transformer.sagemaker_session.default_bucket_prefix,
              transformer._current_job_name,
          )
  
      job_config = sagemaker.transformer._TransformJob._load_config(
          data,
          data_type,
          content_type,
          compression_type,
          split_type,
          transformer,
      )
  
      config = {
          "TransformJobName": transformer._current_job_name,
          "ModelName": transformer.model_name,
          "TransformInput": job_config["input_config"],
          "TransformOutput": job_config["output_config"],
          "TransformResources": job_config["resource_config"],
      }
  
      data_processing = (
          sagemaker.transformer._TransformJob._prepare_data_processing(
              input_filter, output_filter, join_source
          )
      )
      if data_processing is not None:
          config["DataProcessing"] = data_processing
  
      if transformer.strategy is not None:
          config["BatchStrategy"] = transformer.strategy
  
      if transformer.max_concurrent_transforms is not None:
          config[
              "MaxConcurrentTransforms"
          ] = transformer.max_concurrent_transforms
  
      if transformer.max_payload is not None:
          config["MaxPayloadInMB"] = transformer.max_payload
  
      if transformer.env is not None:
          config["Environment"] = transformer.env
  
      if transformer.tags is not None:
          config["Tags"] = transformer.tags
  
      return config
  
imports: from __future__ import print_function, absolute_import
  import os
  import re
  import sagemaker
  from sagemaker import fw_utils, job, utils, s3, session, vpc_utils
  from sagemaker.amazon import amazon_estimator
  from sagemaker.tensorflow import TensorFlow
  from sagemaker.estimator import EstimatorBase
  from sagemaker.processing import Processor
  from sagemaker.utils import format_tags
label_desc: Use image node_id 15 for calling a global function with example usage: transform_config(transformer, data, data_type, content_type, compression_type, split_type, job_name, input_filter, output_filter, join_source) and returns: config